SYSTEM AND METHOD FOR DYNAMIC AUTONOMOUS TRANSACTIONAL IDENTITY MANAGEMENT
A dynamic and autonomous identity management system and method are disclosed that consolidates patient identity data from disparate data sources into a single system that makes patient data easily accessible in a uniform and transparent manner.
1 . An identity management system, comprising:
a computer system;
a data store associated with the computer system, the data store being a graph database and having a plurality of patient records, wherein each patient record contains information about a particular patient, the graph database having a plurality of vertexes with each vertex containing a set of human readable key value pair from a healthcare electronic data exchange transaction and a plurality of edges that interconnect at least one vertex to a second vertex, each edge contains a relationship between the two vertexes connected by the edge and a set of human readable key value pairs encoding properties of the relationship;
an identity management component hosted on the computer system;
the identity management component configured to perform an identity extraction on a piece of input data to determine if the identity of the particular patient is contained in the data store, an identity matching on a plurality of pieces of input data about the particular patient to determine if the plurality of pieces of data describe the particular patient contained in the data store using one of a plurality of matching processes and an identity merging for merging, in the graph database, the plurality of pieces of data that describe the patient contained in the data store to generate a merged property graph model that becomes a vertex of the graph model.
2 . The system of claim 1 , wherein the identity extraction extracts a bag of words consumer model from every transaction for the particular patient.
3 . The system of claim 1 , wherein the identity matching generates a unique match key for each duplicate data for the patient.
4 . The system of claim 1 , wherein the identity merging generates a similarity score for each two pieces of data about the patient.
5 . The system of claim 4 , wherein the identity merging merges the pieces of data in a new vertex with the relationship to the original identity and the match process for the patient when the similarity score exceeds a matching threshold.
6 . The system of claim 4 , wherein the identity merging first merges the pieces of data for the patient with a highest score.
7 . The system of claim 1 , wherein the data store further comprises a graph database.
8 . An identity management method, comprising:
providing a computer system and a data store associated with the computer system, the data store being a graph database and containing a plurality of patient records, wherein each patient record contains information about a particular patient, the graph database having a plurality of vertexes with each vertex containing a human readable key value pair from a healthcare electronic data exchange transaction and a plurality of edges that interconnect at least one vertex to a second vertex, each edge contains a relationship between the two vertexes connected by the edge and a set of human readable key value pairs encoding properties of the relationship;
performing, based on the data store, an identity extraction on a piece of input data to determine if the identity of the particular patient is contained in the data store;
determining, if the plurality of pieces of data describe the particular patient contained in the data store; and
merging, in the graph database, the plurality of pieces of data that describe the particular patient contained in the data store to generate a merged property graph model that becomes a vertex of the graph model.
9 . The method of claim 8 , wherein performing the identity extraction further comprises extracting a bag of words consumer model from every transaction for each particular patient.
10 . The method of claim 8 , wherein determining if the plurality of pieces of data describe the particular patient further comprises generating a unique match key for each duplicate data for the particular patient.
11 . The method of claim 8 , wherein merging further comprises generating a similarity score for each two pieces of data about the particular patient.
12 . The method of claim 11 wherein identity further comprising merging the pieces of data in a new vertex with the relationship to the original identity and the match process for the particular patient when the similarity score exceeds a matching threshold.
13 . The method of claim 11 , wherein the identity merging further comprises first merging the pieces of data for the particular patient with a highest score.
14 . The method of claim 8 , wherein the data store further comprises a graph database.